
import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a         =   logging.get_logger(__name__)

__a         =   {
    'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json',
    'BridgeTower/bridgetower-base-itm-mlm': (
        'https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'
    ),
}
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :Optional[Any]       	=      'bridgetower_vision_model'
	def __init__(			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     str=7_6_8			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=1_2			,				SCREAMING_SNAKE_CASE_    :     str=3			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=1_6			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=2_8_8			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=1			,				SCREAMING_SNAKE_CASE_    :     Tuple=1e-05			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=False			,				SCREAMING_SNAKE_CASE_    :     List[str]=True			,				SCREAMING_SNAKE_CASE_    :     int=False			,				**SCREAMING_SNAKE_CASE_    :     int			,				)			->	Dict:
					super().__init__(**SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   hidden_size
					lowercase_	    =   num_hidden_layers
					lowercase_	    =   num_channels
					lowercase_	    =   patch_size
					lowercase_	    =   image_size
					lowercase_	    =   initializer_factor
					lowercase_	    =   layer_norm_eps
					lowercase_	    =   stop_gradient
					lowercase_	    =   share_layernorm
					lowercase_	    =   remove_last_layer




	@classmethod
	def 		_lowercase    (			cls    :     Any			,				SCREAMING_SNAKE_CASE_    :     Union[str, os.PathLike]			,				**SCREAMING_SNAKE_CASE_    :     List[Any]			)			->	"PretrainedConfig":
					lowercase_    ,      lowercase_	    =   cls.get_config_dict(SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)

					if config_dict.get('''model_type'''			) == "bridgetower":
									lowercase_	    =   config_dict['''text_config''']

					if "model_type" in config_dict and hasattr(cls			,				'''model_type'''			) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
									    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''			)

					return cls.from_dict(SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)

class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :Tuple       	=      'bridgetower_text_model'
	def __init__(			self    :     Any			,				SCREAMING_SNAKE_CASE_    :     List[str]=5_0_2_6_5			,				SCREAMING_SNAKE_CASE_    :     List[Any]=7_6_8			,				SCREAMING_SNAKE_CASE_    :     Any=1_2			,				SCREAMING_SNAKE_CASE_    :     Dict=1_2			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=1			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=3_0_7_2			,				SCREAMING_SNAKE_CASE_    :     Tuple="gelu"			,				SCREAMING_SNAKE_CASE_    :     Tuple=0.1			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=0.1			,				SCREAMING_SNAKE_CASE_    :     Tuple=5_1_4			,				SCREAMING_SNAKE_CASE_    :     List[str]=1			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=1e-05			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=1			,				SCREAMING_SNAKE_CASE_    :     Dict=0			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=2			,				SCREAMING_SNAKE_CASE_    :     Optional[int]="absolute"			,				SCREAMING_SNAKE_CASE_    :     Tuple=True			,				**SCREAMING_SNAKE_CASE_    :     Optional[int]			,				)			->	str:
					super().__init__(**SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   vocab_size
					lowercase_	    =   hidden_size
					lowercase_	    =   num_hidden_layers
					lowercase_	    =   num_attention_heads
					lowercase_	    =   hidden_act
					lowercase_	    =   initializer_factor
					lowercase_	    =   intermediate_size
					lowercase_	    =   hidden_dropout_prob
					lowercase_	    =   attention_probs_dropout_prob
					lowercase_	    =   max_position_embeddings
					lowercase_	    =   type_vocab_size
					lowercase_	    =   layer_norm_eps
					lowercase_	    =   position_embedding_type
					lowercase_	    =   use_cache
					lowercase_	    =   pad_token_id
					lowercase_	    =   bos_token_id
					lowercase_	    =   eos_token_id




	@classmethod
	def 		_lowercase    (			cls    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     Union[str, os.PathLike]			,				**SCREAMING_SNAKE_CASE_    :     Any			)			->	"PretrainedConfig":
					lowercase_    ,      lowercase_	    =   cls.get_config_dict(SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)

					if config_dict.get('''model_type'''			) == "bridgetower":
									lowercase_	    =   config_dict['''text_config''']

					if "model_type" in config_dict and hasattr(cls			,				'''model_type'''			) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
									    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''			)

					return cls.from_dict(SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)

class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :List[Any]       	=      'bridgetower'
	def __init__(			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     str=True			,				SCREAMING_SNAKE_CASE_    :     Tuple="gelu"			,				SCREAMING_SNAKE_CASE_    :     int=7_6_8			,				SCREAMING_SNAKE_CASE_    :     Any=1			,				SCREAMING_SNAKE_CASE_    :     Any=1e-05			,				SCREAMING_SNAKE_CASE_    :     List[Any]=False			,				SCREAMING_SNAKE_CASE_    :     Any="add"			,				SCREAMING_SNAKE_CASE_    :     List[Any]=1_2			,				SCREAMING_SNAKE_CASE_    :     List[Any]=6			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=False			,				SCREAMING_SNAKE_CASE_    :     List[Any]=False			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=None			,				SCREAMING_SNAKE_CASE_    :     str=None			,				**SCREAMING_SNAKE_CASE_    :     Dict			,				)			->	Union[str, Any]:
					# TODO: remove this once the Hub files are updated.
					lowercase_	    =   kwargs.pop('''text_config_dict'''			,				SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   kwargs.pop('''vision_config_dict'''			,				SCREAMING_SNAKE_CASE_			)

					super().__init__(**SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   share_cross_modal_transformer_layers
					lowercase_	    =   hidden_act
					lowercase_	    =   hidden_size
					lowercase_	    =   initializer_factor
					lowercase_	    =   layer_norm_eps
					lowercase_	    =   share_link_tower_layers
					lowercase_	    =   link_tower_type
					lowercase_	    =   num_attention_heads
					lowercase_	    =   num_hidden_layers
					lowercase_	    =   tie_word_embeddings
					lowercase_	    =   init_layernorm_from_vision_encoder

					if text_config is None:
									lowercase_	    =   {}
									logger.info('''`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.'''			)

					if vision_config is None:
									lowercase_	    =   {}
									logger.info('''`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.'''			)

					lowercase_	    =   BridgeTowerTextConfig(**SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   BridgeTowerVisionConfig(**SCREAMING_SNAKE_CASE_			)
	@classmethod
	def 		_lowercase    (			cls    :     str			,				SCREAMING_SNAKE_CASE_    :     BridgeTowerTextConfig			,				SCREAMING_SNAKE_CASE_    :     BridgeTowerVisionConfig			,				**SCREAMING_SNAKE_CASE_    :     Optional[int]			)			->	List[str]:
					return cls(text_config=text_config.to_dict()			,				vision_config=vision_config.to_dict()			,				**SCREAMING_SNAKE_CASE_			)




	def 		_lowercase    (			self    :     Optional[int]			)			->	List[Any]:
					lowercase_	    =   copy.deepcopy(self.__dict__			)
					lowercase_	    =   self.text_config.to_dict()
					lowercase_	    =   self.vision_config.to_dict()
					lowercase_	    =   self.__class__.model_type
					return output










"""simple docstring"""




import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


lowerCAmelCase				:     Dict					=				namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def        a__					(							snake_case__			,					snake_case__							)		->   Optional[Any]:
			return (abs(source - target							) / target) < 0.01



@pytest.mark.integration
def        a__					(							snake_case__							)		->   int:
			lowerCamelCase      =   _TestCommandArgs(dataset=snake_case__			,					all_configs=snake_case__			,					save_infos=snake_case__							)
			lowerCamelCase      =   TestCommand(*snake_case__							)
			test_command.run()
			lowerCamelCase      =   os.path.join(snake_case__			,					"""README.md"""							)
			assert os.path.exists(snake_case__							)
			lowerCamelCase      =   DatasetInfosDict.from_directory(snake_case__							)
			lowerCamelCase      =   DatasetInfosDict(
			    {
			        """default""": DatasetInfo(
			            features=Features(
			                {
			                    """tokens""": Sequence(Value("""string"""							)							),
			                    """ner_tags""": Sequence(
			                        ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""]							)							),
			                    """langs""": Sequence(Value("""string"""							)							),
			                    """spans""": Sequence(Value("""string"""							)							),
			                }							)			,					splits=[
			                {
			                    """name""": """train""",
			                    """num_bytes""": 2_35_15_63,
			                    """num_examples""": 1_00_00,
			                },
			                {
			                    """name""": """validation""",
			                    """num_bytes""": 23_84_18,
			                    """num_examples""": 10_00,
			                },
			            ]			,					download_size=3_94_06_80			,					dataset_size=2_58_99_81			,					)
			    }							)
			assert dataset_infos.keys() == expected_dataset_infos.keys()
			for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
						lowerCamelCase ,  lowerCamelCase      =   getattr(dataset_infos["""default"""]			,					snake_case__							), getattr(expected_dataset_infos["""default"""]			,					snake_case__							)
						if key == "num_bytes":
									assert is_apercent_close(snake_case__			,					snake_case__							)
						elif key == "splits":
									assert list(snake_case__							) == list(snake_case__							)
									for split in result:
												assert result[split].name == expected[split].name
												assert result[split].num_examples == expected[split].num_examples
												assert is_apercent_close(result[split].num_bytes			,					expected[split].num_bytes							)
						else:
									result == expected



